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首页> 外文期刊>Future generation computer systems >'Cheap grid': Leveraging system failure using stochastic computation
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'Cheap grid': Leveraging system failure using stochastic computation

机译：“便宜的网格”：利用随机计算来利用系统故障

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摘要

Traditionally, network and computation failure on a heterogeneous network are viewed as an unfortunate obstacle to reliable, efficient computation. We propose that such noise can be incorporated into the algorithm design as part of the necessary source of randomness used in stochastic computation. This paradigm incorporates network and computation failure at a high level in the solution-discovery algorithm, rather than attempting to hide and suppress all such noise at the lowest possible levels in the computation tool. This idea enables the creation of a network solution system with extremely small amounts of global state. This lack of required system state allows for heightened degrees of scalability in the computation engine, and fewer resources are consumed by system management. Algorithms with a stochastic component are easily adapted to this system; various types of evolutionary computation are particularly well adapted to this hybrid paradigm. A specific example using a modified steady-state genetic algorithm is provided to explore the functionality of the resulting composite system. The developed architecture is used to calculate the solution to a number of problems, in each case converging on a solution measurably faster than that of a fully "fault-tolerant" scheme, thereby resulting in lower overhead and faster execution time.
机译：传统上，异构网络上的网络和计算故障被视为可靠，高效计算的不幸障碍。我们建议可以将这种噪声纳入算法设计中，作为随机计算中使用的必要随机性源的一部分。这种范例在解决方案发现算法中将网络和计算故障纳入了较高的层次，而不是试图在计算工具中以尽可能低的水平隐藏和抑制所有此类噪声。这个想法可以创建具有极少量全局状态的网络解决方案系统。由于缺少所需的系统状态，因此可以提高计算引擎的可伸缩性程度，并且系统管理消耗的资源更少。具有随机成分的算法很容易适应该系统。各种类型的进化计算特别适合这种混合范例。提供了使用修改后的稳态遗传算法的特定示例，以探索所得复合系统的功能。所开发的体系结构用于计算许多问题的解决方案，在每种情况下，其收敛速度明显高于完全“容错”方案的解决方案，从而降低了开销并缩短了执行时间。

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